Pair production of neutralinos via photon-photon collisions

We investigated the production of neutralino pairs via photon-photon collisions in the minimal supersymmetric model(MSSM) at future linear colliders. The numerical analysis of their production rates is carried out in the mSUGRA scenario. The results show that this cross section can reach about 18 femto barn for $\tilde{\chi}^{0}_{1} \tilde{\chi}^{0}_{2}$ pair production and 9 femto barn for $\tilde{\chi}^{0}_{2}\tilde{\chi}^{0}_{2}$ pair production with our chosen input parameters.Comment: LaTex File, 3 EPS Files, 17 page

EAST: An Efficient and Accurate Scene Text Detector

Previous approaches for scene text detection have already achieved promising performances across various benchmarks. However, they usually fall short when dealing with challenging scenarios, even when equipped with deep neural network models, because the overall performance is determined by the interplay of multiple stages and components in the pipelines. In this work, we propose a simple yet powerful pipeline that yields fast and accurate text detection in natural scenes. The pipeline directly predicts words or text lines of arbitrary orientations and quadrilateral shapes in full images, eliminating unnecessary intermediate steps (e.g., candidate aggregation and word partitioning), with a single neural network. The simplicity of our pipeline allows concentrating efforts on designing loss functions and neural network architecture. Experiments on standard datasets including ICDAR 2015, COCO-Text and MSRA-TD500 demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of both accuracy and efficiency. On the ICDAR 2015 dataset, the proposed algorithm achieves an F-score of 0.7820 at 13.2fps at 720p resolution.Comment: Accepted to CVPR 2017, fix equation (3

The effects of large extra dimensions on associated ttˉh0t\bar{t} h^0 production at linear colliders

In the framework of the large extra dimensions (LED) model, the effects of LED on the processes \rrtth and \eetth at future linear colliders are investigated in both polarized and unpolarized collision modes. The results show that the virtual Kaluza-Klein (KK) graviton exchange can significantly modify the standard model expectations for these processes with certain polarizations of initial states. The process \rrtth with $\sqrt{s}=3.5 TeV$ allows the effective scale $\Lambda_T$ to be probed up to 7.8 and 8.6 TeV in the unpolarized and $P_{\gamma} = 0.9$, J=2 polarized $\gamma \gamma$ collision modes, respectively. For the \eetth process with $\sqrt{s}=3.5 TeV$, the upper limits of $\Lambda_T$ to be observed can be 6.7 and 7.0 TeV in the unpolarized and $P_{e^+} = 0.6$, $P_{e^-} = 0.8$, $-+$ polarized $e^+e^-$ collision modes, respectively. We find the \rrtth channel in J=2 polarized photon collision mode provides a possibility to improve the sensitivity to the graviton tower exchange.Comment: To be appeard in Physical Review

TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis

Time series anomaly detection is a challenging problem due to the complex temporal dependencies and the limited label data. Although some algorithms including both traditional and deep models have been proposed, most of them mainly focus on time-domain modeling, and do not fully utilize the information in the frequency domain of the time series data. In this paper, we propose a Time-Frequency analysis based time series Anomaly Detection model, or TFAD for short, to exploit both time and frequency domains for performance improvement. Besides, we incorporate time series decomposition and data augmentation mechanisms in the designed time-frequency architecture to further boost the abilities of performance and interpretability. Empirical studies on widely used benchmark datasets show that our approach obtains state-of-the-art performance in univariate and multivariate time series anomaly detection tasks. Code is provided at https://github.com/DAMO-DI-ML/CIKM22-TFAD.Comment: Accepted by the ACM International Conference on Information and Knowledge Management (CIKM 2022